IRB 80-M-0083 NCT00001174 This collaboration has developed a bipolar disorder family resource that has proven to be uniquely valuable in testing clinically-based hypotheses about the genetic heterogeneity of the illness. We have ascertained and assessed about 150 nuclear families with a bipolar I proband and two or more siblings with a major affective disorder. One important goal is to derive and test genetically meaningful clinical subtypes. We previously generated a database of clinical variables, The Bipolar Disorder Phenome Database, based in part on this sample. The database contains several hundred clinical variables, harmonized with those collected by the NIMH Genetics Initiative, describing the course, symptoms, and clinical picture of bipolar disorder. We have made the database available to the scientific community to support research into the genetics of bipolar disorder and related conditions. In collaboration with scientists at Cold Spring Harbor Laboratory and UCSD, we have used cutting-edge genetic tools to search for relatively small structural variations in chromosomes, known as copy number variants (CNVs), that may play a role in bipolar disorder. Using a set of trios we collected with collaborators, we found evidence that some CNVs strongly over-represented among people with schizophrenia may also play a role in bipolar disorder, especially in an early-onset form. In the coming year, we plan to perform exome sequencing on the same set of trios in order to detect damaging insertions, deletions, and point mutations that arise de novo and may contribute to the risk for bipolar disorder or modify its clinical presentation. In order to better interpret the impact of genetic variation on the brain biology of bipolar disorder, we are pursuing a variety of functional genomics studies, including brain imaging, microarray gene expression and RNA-seq in post-mortem brain tissue, and cellular phenotyping of neurons derived from induced pluripotent stem cells. Neuroimaging genetics work over the past year has focused on structural imaging as well as positron emission tomography (PET). We contributed data to the ENIGMA brain imaging consortium, which detected SNPs associated with hippocampal volume. This important endophenotype may shed light on the mechanisms whereby common genetic variants influence risk for psychiatric disorders. We also used magnetic resonance imaging (MRI) data from volunteers with major depressive or bipolar disorder, along with healthy volunteers, to assess the heritability of corpus callosum(CC) size and the genetic correlations among anatomical sub regions of the CC among individuals with and without mood disorders. Significant heritability was found for several sub-regions, with strong and significant genetic correlations among most sub-regions. Distinct genetic factors seem to be involved in caudal and rostral regions, consistent with the divergent functional specialization of the brain areas. In the coming year we plan to use genome-wide SNP data to assess heritability and genetic correlations of brain imaging phenotypes, exploiting new developments in genetic identity-by-state methods. We have also carried out the first study to use the new technique of RNA-seq to characterize the transcribed portion of the genome (transcriptome) in bipolar disorder. The resulting data were analyzed using several bioinformatics tools on high-performance computers at NIH. This study highlighted several genes and transcripts that were involved in dysregulation of neuroplasticity, circadian rhythms and second-messenger systems in bipolar disorder. In the coming year we will analyze the data for allele-specific expression, extended brain-specific 3 untranslated regions, (UTRs) novel, brain-expressed genes that may be involved in bipolar disorder. Over the past year we have also begun experiments aimed at modeling functional genomics of disease-related genes in cells derived from induced pluripotent stem cell (iPSC) lines. This project, led by Staff Scientist Sevilla-Detera-Wadleigh, aims to explore the ways in which we can use iPSC technology to study the biological impact of genes and genetic mutations that we identify in our other ongoing studies. Working with the NIH Center for Regenerative Medicine and the iPSC core of NHLBI we have so far successfully reprogrammed fibroblasts from 5 different individuals; we are also studying lines reprogrammed in collaborating labs. We are differentiating the cells into neurons and glia, and characterizing their morphology, action potential, gene expression profiles, and response to drugs and toxins. Careful analysis of these phenotypes could reveal differences between control and patient-derived cells, but we are also exploring ways to measure the functional impact of genetic mutations at the cellular level and to use genome editing tools such as TALENs to assess rescue phenotypes.